quercus

Overview

The quercus dataset contains presence and absence records of eight European Quercus (oak) species combined with 31 environmental predictor variables covering bioclimatic, topographic, vegetation, and human impact dimensions.

Its main features are:

• Complete case study - No missing values, clean data ready for analysis.
• Multi-class classification - Eight oak species plus absence points (9 levels).
• European scope - Records spanning western, central, and southern Europe.
• Ecological predictors - Climate, topography, vegetation indices, land cover, and human footprint.

This dataset was built to support a range of analytical approaches, such as binary and multi-class classification, niche modelling, and niche overlap analysis.

Setup

The following R libraries are required to run this tutorial:

The code below loads the example data.

data(
  quercus,
  quercus_response,
  quercus_predictors,
  package = "spatialData"
)

Data Structure

The dataset is an sf data frame with 6728 rows and 33 columns, and no missing data. The first 10 records and all columns but geometry are shown below.

DT::datatable(
  data = head(sf::st_drop_geometry(quercus), n = 10),
  rownames = FALSE
)

Response Variable

The response variable is species, a categorical variable with 9 levels (8 species + absence):

table(quercus$species)
#> 
#>           absence    Quercus cerris   Quercus faginea      Quercus ilex 
#>              1899               394               287               627 
#>   Quercus petraea Quercus pubescens Quercus pyrenaica     Quercus robur 
#>              1445               225               133              1660 
#>     Quercus suber 
#>                58

Predictor Variables

The dataset includes 31 predictor variables organized into six categories:

Category	Variables	Source
Bioclimatic (17)	bio1, bio2, …, bio19 (excluding bio8, bio9)	WorldClim
NDVI (4)	ndvi_average, ndvi_maximum, ndvi_minimum, ndvi_range	MODIS
Solar radiation (4)	sun_rad_average, sun_rad_maximum, sun_rad_minimum, sun_rad_range	WorldClim
Land cover (3)	landcover_veg_bare, landcover_veg_herb, landcover_veg_tree	MODIS MOD44B
Topographic (2)	topo_slope, topographic_diversity	SRTM
Human impact (1)	human_footprint	Venter et al. 2016

Interactive Map

The map below shows the spatial distribution of oak species and absence points across Europe:

mapview::mapview(
  quercus,
  zcol = "species",
  layer.name = "Species"
)

Example Usage

This example shows how quercus can be used to identify the environmental conditions that distinguish oak species using multicollinearity filtering and a decision tree.

Multicollinearity Filtering

Many predictors in this dataset are correlated (e.g., bio5, bio6, bio7 are all derived from temperature extremes). The collinear::collinear() function from the R package collinear selects a non-redundant subset that maximizes predictive power for the response variable.

predictors_selection <- collinear::collinear(
  df = quercus,
  responses = "species",
  predictors = quercus_predictors,
  f = collinear::f_categorical_rf,
  quiet = TRUE
)

predictors_selection$species$selection
#> [1] "sun_rad_minimum"       "bio14"                 "bio4"                 
#> [4] "landcover_veg_bare"    "ndvi_maximum"          "topographic_diversity"
#> [7] "bio16"                 "human_footprint"       "landcover_veg_tree"   
#> attr(,"validated")
#> [1] TRUE

Decision Tree Model

Species occurrence counts are imbalanced — Q. robur has 1,660 records while Q. suber has only 58. Without correction, rare species get absorbed into leaves dominated by common ones. The collinear::case_weights() function generates inverse-frequency weights so that each species contributes equally to the tree splits.

m <- rpart::rpart(
  formula = predictors_selection$species$formulas$classification,
  data = sf::st_drop_geometry(quercus),
  method = "class",
  weights = collinear::case_weights(x = quercus$species)
)

Tree Visualization

The decision tree below shows which environmental variables and thresholds best separate oak species. Each split represents a condition (e.g., “bio6 < -2.3”), and each leaf shows the most likely species at that combination of conditions.

rpart.plot::rpart.plot(
  m,
  type = 0,
  extra = 8,
  fallen.leaves = TRUE,
  roundint = FALSE,
  cex = 0.7,
  box.palette = as.list(viridis::turbo(n = 9, alpha = 0.5)),
  main = "Decision Tree: European Oak Species Classification"
)

The figure shows how different combinations of environmental conditions lead to the dominance of each Quercus species.

Prediction Map

Assigning tree predictions back to the spatial data reveals the locations where each species has the highest probability to appear.

quercus$predicted <- predict(
  object = m,
  newdata = sf::st_drop_geometry(quercus),
  type = "class"
)

mapview::mapview(
  quercus,
  zcol = "predicted",
  layer.name = "Predicted species",
  col.regions = viridis::turbo(
    n = length(unique(quercus$predicted))
  )
)

Species Probability Map

The package provides the raster dataset quercus_env to predict models based on the quercus data.

quercus_env <- spatialData::quercus_extra()
class(quercus_env)
#> [1] "SpatRaster"
#> attr(,"package")
#> [1] "terra"
names(quercus_env)
#>  [1] "bio1"                  "bio10"                 "bio11"                
#>  [4] "bio12"                 "bio13"                 "bio14"                
#>  [7] "bio15"                 "bio16"                 "bio17"                
#> [10] "bio18"                 "bio19"                 "bio2"                 
#> [13] "bio3"                  "bio4"                  "bio5"                 
#> [16] "bio6"                  "bio7"                  "human_footprint"      
#> [19] "landcover_veg_bare"    "landcover_veg_herb"    "landcover_veg_tree"   
#> [22] "ndvi_average"          "ndvi_maximum"          "ndvi_minimum"         
#> [25] "ndvi_range"            "sun_rad_average"       "sun_rad_maximum"      
#> [28] "sun_rad_minimum"       "sun_rad_range"         "topo_slope"           
#> [31] "topographic_diversity"

plot(
  x = quercus_env[["bio1"]],
  col = viridis::turbo(n = 100)
  )

The map below shows the fitted probability of Quercus petraea across Europe.

quercus_probability <- predict(
  object = quercus_env,
  model = m,
  type = "prob",
  na.rm = TRUE
)

plot(
  x = quercus_probability[["Quercus.petraea"]],
  col = viridis::viridis(n = 5)
  )

The decision tree provides interpretable rules for species separation, but more flexible methods like random forests or gradient boosting may achieve higher accuracy and more visually pleasant results.